code: def __init__(
      self,
      txt_file,  # path to the text file containing the list of image paths
      data_root,  # root directory of the LSUN dataset
      size=None,  # the size of images to resize to
      interpolation="bicubic",  # interpolation method to be used while resizing
      flip_p=0.5,  # probability of random horizontal flipping
  ):
      self.data_paths = (
          txt_file  # store path to text file containing list of images
      )
      self.data_root = (
          data_root  # store path to root directory of the dataset
      )
      with open(
          self.data_paths, "r"
      ) as f:  # open and read the text file
          self.image_paths = (
              f.read().splitlines()
          )  # read the lines of the file and store as list
      self._length = len(self.image_paths)  # store the number of images
  
      # create dictionary to hold image path information
      self.labels = {
          "relative_file_path_": [l for l in self.image_paths],
          "file_path_": [
              os.path.join(self.data_root, l) for l in self.image_paths
          ],
      }
  
      # set the image size to be resized
      self.size = size
      # set the interpolation method for resizing the image
      self.interpolation = {
          "linear": PIL.Image.LINEAR,
          "bilinear": PIL.Image.BILINEAR,
          "bicubic": PIL.Image.BICUBIC,
          "lanczos": PIL.Image.LANCZOS,
      }[interpolation]
      # randomly flip the image horizontally with a given probability
      self.flip = transforms.RandomHorizontalFlip(p=flip_p)
  
imports: import os
  import numpy
  import PIL
  from PIL import Image
  from torch.utils.data import Dataset
  from torchvision import transforms
label_desc: Use image node_id 1 to create a new LSUNBase object from inherited base classes: Dataset with example: obj = LSUNBase(txt_file, data_root, size, interpolation, flip_p)